Connecting the dots: Ocean research and public policy
Bibliographic record
Abstract
Canada has gained a world-leading position in the science and technology of cabled ocean observing systems, principally through the federal and British Columbia (BC) government investments ($120M) in the VENUS and NEPTUNE Canada ocean observatories, now deployed in coastal to deep ocean waters off BC's West Coast. The combination of continuous power, high bandwidth and real-time data streaming make the VENUS and NEPTUNE Canada observatories transformative in their capacity to support research applications to key areas of public policy, including environmental monitoring, hazard mitigation, resource assessment, and sovereignty and security. While deployed off the West Coast, the technologies are applicable in other settings including the Arctic. Ocean Networks Canada (ONC) was created as a not-for-profit agency by the University of Victoria in 2007 to manage and develop the VENUS and NEPTUNE Canada observatories and their applications to public policy, commercial development, and public outreach. To advance its mandate, ONC was recently named as a federal Centre of Excellence in Commercialization and Research. The public policy initiatives of ONC, in the context of its broader strategic plan, have included: (a) a review of Canadian federal and provincial policy initiatives and priorities and their relationship to the data types generated by the NEPTUNE Canada and VENUS research programs; (b) planning of workshops with federal science-based departments and agencies to be held later in 2010; (c) discussions with BC government departments related to their emerging Ocean and Coastal Strategy; and, preparation of a discussion paper for federal government departments on application of cabled observatory technologies to the Arctic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.026 | 0.025 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.031 | 0.017 |
| Insufficient payload (model declined to judge) | 0.035 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".